Frequently Asked questions about the AirFlow class library in Python

Airflow is an open source platform for arranging and managing data workflows.It provides a simple and reliable way to schedule, monitor and maintain complex workflow.In the process of using Airflow, developers may encounter some common problems.Here are some common answers to the AirFlow class library. Question 1: What is Airflow and what does it do? Answer: Airflow is an open source work process management platform developed by Airbnb.It can help developers to arrange and manage data workflows, so that data flows between various tasks, thereby achieving automation of data processing and analysis.Through AirFlow, developers can define the dependency relationship between tasks, schedule the execution time of the task, and monitor the execution of the task. Question 2: How to install AirFlow? Answer: You can install AirFlow through the PIP command.Run the following command in the command line to install the latest version of AirFlow: pip install apache-airflow Question 3: How to create and dispatch a workflow? Answer: In AirFlow, the workflow is called Dag (Direct ACYCLIC GRAPH, there is a ringless diagram).Developers can define DAG by writing the Python code and specify the dependent relationship between tasks in the code.Then, you can use the AirFlow Web server interface or command line tool to schedule and monitor the execution of the workflow. The following is a simple example, showing how to create a simple DAG: python from airflow import DAG from airflow.operators.bash_operator import BashOperator from datetime import datetime, timedelta default_args = { 'owner': 'airflow', 'depends_on_past': False, 'start_date': datetime(2022, 1, 1), 'retries': 1, 'retry_delay': timedelta(minutes=5), } dag = DAG( 'my_dag', default_args=default_args, description='A simple AirFlow DAG', schedule_interval=timedelta(days=1), ) t1 = BashOperator( task_id='task_1', bash_command='echo "Task 1"', dag=dag, ) t2 = BashOperator( task_id='task_2', bash_command='echo "Task 2"', dag=dag, ) t1 >> t2 In the above code, we created a DAG called `my_dag`.The DAG contains two tasks: `task_1` and` task_2`.`task_2` depends on` task_1`, which means that after the execution of `task_1` is completed, it will only execute` task_2`.This dependencies are represented by the operators of the operators. Question 4: How to configure AirFlow? Answer: The configuration file of Airflow is located in `$ Airflow_home/Airflow.cfg`.Each parameter in the configuration file can be modified as needed to meet specific needs.Some of the common parameters in the configuration file are as follows: -`dags_folder`: specify the folder path that stores the DAG file. -`executor`: Specify the actuator of AirFlow, you can choose Localexecutor, Cleryexecutor, etc. -` sql_alchemy_conn`: Specify a database connection for storing the AirFlow metadata. Question 5: How to debug the AirFlow task? Answer: A common way to debug the AirFlow task is to use the log.Airflow generates detailed log records, which can locate the problem by viewing the log.You can set the log level in the configuration file of Airflow so that more detailed information can be recorded. Another debugging method is to use Airflow's `Airflow Test` command.This command can manually run a specific task and check the output results of the task execution. Summarize: Airflow is a powerful workflow management platform for arranging and managing data workflows.When using AirFlow, developers may encounter some common problems, such as installing AirFlow, creating and scheduling workflow, configuration of AirFlow, and debugging tasks.By reading documents and using community resources carefully, developers can solve these problems and make full use of the powerful functions of Airflow.